Efficient Screening of Coformers for Active Pharmaceutical Ingredient Cocrystallization

Abstract

Controlling the physical properties of solid forms for active pharmaceutical ingredients (APIs) through cocrystallization is an important part of drug product development. However, it is difficult to know a priori which coformers will form cocrystals with a given API, and the current state-of-the-art for cocrystal discovery involves an expensive, time-consuming, and, at the early stages of pharmaceutical development, API material-limited experimental screen. We propose a systematic, high-throughput computational approach primarily aimed at identifying API/coformer pairs that are unlikely to lead to experimentally observable cocrystals and can therefore be eliminated with only a brief experimental check, from any experimental investigation. On the basis of a well-established crystal structure prediction (CSP) methodology, the proposed approach derives its efficiency by not requiring any expensive quantum mechanical calculations beyond those already performed for the CSP investigation of the neat API itself. The approach and assumptions are tested through a computational investigation on 30 potential 1:1 multicomponent systems (cocrystals and solvate) involving 3 active pharmaceutical ingredients and 9 coformers and one solvent. This is complemented with a detailed experimental investigation of all 30 pairs, which led to the discovery of five new cocrystals (three API−coformer combinations, a polymorphic cocrystal example, and one with different stoichiometries) and a cis-aconitic acid polymorph. The computational approach indicates that, for some APIs, a significant proportion of all potential API/coformer pairs could be investigated with only a brief experimental check, thereby saving considerable experimental effort.

Virtual Screening, Structural Analysis, and Formation Thermodynamics of Carbamazepine Cocrystals

“…Hence, a number of more reliable quantitative computational tools for coformer selection have been presented. These may be generally classified [ 16 ] as knowledge-based (both structural informatics [ 17 , 18 ] and thermodynamic methods [ 19 , 20 ]), physics-based (Hansen solubility parameters [ 21 ], molecular electrostatic potential map [ 22 , 23 ], COSMO-RS [ 24 ], and crystal structure prediction [ 25 , 26 , 27 ]), and machine learning methods [ 28 , 29 , 30 , 31 , 32 ]. Each of the listed techniques has its own merits and drawbacks, while the success rate of prediction results may vary significantly, depending on an API and the size of a test set [ 33 ].…”

Exploring the Cocrystal Landscape of Posaconazole by Combining High-Throughput Screening Experimentation with Computational Chemistry

“…19 Physicsbased methods describe the compound on a quantum chemical level and include the conductor-like screening model for real solvents (COSMO-RS), 20,21 molecular electrostatic potential surfaces (MEPS), 22,23 and crystal structure prediction (CSP). 14, 24 ML methods, such as artificial neural networks, 25,26 employ large cocrystal databases to train predictive models for the ranking of promising coformers. 27,28 More recently, several studies have reported on the combined use of multiple virtual approaches in the coformer selection for APIs.…”

“…14 CSP is able to evaluate both terms but at the expense of power-demanding quantum chemical calculations that can last for days. 24 Therefore, the choice of suitable virtual tools is a trade-off between accuracy and computational demands.…”

“…Knowledge-based methods rely on cocrystal structure information contained in the Cambridge Structural Database (CSD) to select coformers according to their shape [molecular complementarity (MC)], , or probability of formation of intermolecular interactions [hydrogen bond propensity (HBP)] . Physics-based methods describe the compound on a quantum chemical level and include the conductor-like screening model for real solvents (COSMO-RS), , molecular electrostatic potential surfaces (MEPS), , and crystal structure prediction (CSP). , ML methods, such as artificial neural networks, , employ large cocrystal databases to train predictive models for the ranking of promising coformers. , More recently, several studies have reported on the combined use of multiple virtual approaches in the coformer selection for APIs. − …”

“…Despite the diverse methods proposed, most of them account only for the miscibility between the target molecule and the coformer, neglecting the contributions of the crystal structure . CSP is able to evaluate both terms but at the expense of power-demanding quantum chemical calculations that can last for days . Therefore, the choice of suitable virtual tools is a trade-off between accuracy and computational demands.…”